Popular computational methods to assess multiprotein complexes derived from label-free affinity purification and mass spectrometry (AP-MS) experiments

Bidirectional linkage of DNA barcodes for the multiplexed mapping of higher-order protein interactions in cells

Capturing the full complexity of the diverse hierarchical interactions in the protein interactome is challenging. Here we report a DNA-barcoding method for the multiplexed mapping of pairwise and higher-order protein interactions and their dynamics within cells. The method leverages antibodies conjugated with barcoded DNA strands that can bidirectionally hybridize and covalently link to linearize closely spaced interactions within individual 3D protein complexes, encoding and decoding the protein constituents and the interactions among them. By mapping protein interactions in cancer cells and normal cells, we found that tumour cells exhibit a larger diversity and abundance of protein complexes with higher-order interactions. In biopsies of human breast-cancer tissue, the method accurately identified the cancer subtype and revealed that higher-order protein interactions are associated with cancer aggressiveness.

Multiparameter screen optimizes immunoprecipitation

Immunoprecipitation (IP) coupled with mass spectrometry effectively maps protein-protein interactions when genome-wide, affinity-tagged cell collections are used. Such studies have recorded significant portions of the compositions of physiological protein complexes, providing draft 'interactomes'; yet many constituents of protein complexes still remain uncharted. This gap exists partly because high-throughput approaches cannot optimize each IP. A key challenge for IP optimization is stabilizing in vivo interactions during the transfer from cells to test tubes; failure to do so leads to the loss of genuine interactions during the IP and subsequent failure to detect. Our high-content screening method explores the relationship between in vitro chemical conditions and IP outcomes, enabling rapid empirical optimization of conditions for capturing target macromolecular assemblies.

Interactomics in plant defence: progress and opportunities

Interactomics is a branch of systems biology that deals with the study of protein-protein interactions and how these interactions influence phenotypes. Identifying the interactomes involved during host-pathogen interaction events may bring us a step closer to deciphering the molecular mechanisms underlying plant defence. Here, we conducted a systematic review of plant interactomics studies over the last two decades and found that while a substantial progress has been made in the field, plant-pathogen interactomics remains a less-travelled route. As an effort to facilitate the progress in this field, we provide here a comprehensive research pipeline for an in planta plant-pathogen interactomics study that encompasses the in silico prediction step to the validation step, unconfined to model plants. We also highlight four challenges in plant-pathogen interactomics with plausible solution(s) for each.

Trans-Proteomic Pipeline: Robust Mass Spectrometry-Based Proteomics Data Analysis Suite

The Trans-Proteomic Pipeline (TPP) mass spectrometry data analysis suite has been in continual development and refinement since its first tools, PeptideProphet and ProteinProphet, were published 20 years ago. The current release provides a large complement of tools for spectrum processing, spectrum searching, search validation, abundance computation, protein inference, and more. Many of the tools include machine-learning modeling to extract the most information from data sets and build robust statistical models to compute the probabilities that derived information is correct. Here we present the latest information on the many TPP tools, and how TPP can be deployed on various platforms from personal Windows laptops to Linux clusters and expansive cloud computing environments. We describe tutorials on how to use TPP in a variety of ways and describe synergistic projects that leverage TPP. We conclude with plans for continued development of TPP.

Recent advances in proteomics and metabolomics in plants

Over the past decade, systems biology and plant-omics have increasingly become the main stream in plant biology research. New developments in mass spectrometry and bioinformatics tools, and methodological schema to integrate multi-omics data have leveraged recent advances in proteomics and metabolomics. These progresses are driving a rapid evolution in the field of plant research, greatly facilitating our understanding of the mechanistic aspects of plant metabolisms and the interactions of plants with their external environment. Here, we review the recent progresses in MS-based proteomics and metabolomics tools and workflows with a special focus on their applications to plant biology research using several case studies related to mechanistic understanding of stress response, gene/protein function characterization, metabolic and signaling pathways exploration, and natural product discovery. We also present a projection concerning future perspectives in MS-based proteomics and metabolomics development including their applications to and challenges for system biology. This review is intended to provide readers with an overview of how advanced MS technology, and integrated application of proteomics and metabolomics can be used to advance plant system biology research.

Prediction of Protein-Protein Interactions in Arabidopsis, Maize, and Rice by Combining Deep Neural Network With Discrete Hilbert Transform

Protein-protein interactions (PPIs) in plants play an essential role in the regulation of biological processes. However, traditional experimental methods are expensive, time-consuming, and need sophisticated technical equipment. These drawbacks motivated the development of novel computational approaches to predict PPIs in plants. In this article, a new deep learning framework, which combined the discrete Hilbert transform (DHT) with deep neural networks (DNN), was presented to predict PPIs in plants. To be more specific, plant protein sequences were first transformed as a position-specific scoring matrix (PSSM). Then, DHT was employed to capture features from the PSSM. To improve the prediction accuracy, we used the singular value decomposition algorithm to decrease noise and reduce the dimensions of the feature descriptors. Finally, these feature vectors were fed into DNN for training and predicting. When performing our method on three plant PPI datasets Arabidopsis thaliana, maize, and rice, we achieved good predictive performance with average area under receiver operating characteristic curve values of 0.8369, 0.9466, and 0.9440, respectively. To fully verify the predictive ability of our method, we compared it with different feature descriptors and machine learning classifiers. Moreover, to further demonstrate the generality of our approach, we also test it on the yeast and human PPI dataset. Experimental results anticipated that our method is an efficient and promising computational model for predicting potential plant-protein interacted pairs.

Mapping the plant proteome: tools for surveying coordinating pathways

Plants rapidly respond to environmental fluctuations through coordinated, multi-scalar regulation, enabling complex reactions despite their inherently sessile nature. In particular, protein post-translational signaling and protein-protein interactions combine to manipulate cellular responses and regulate plant homeostasis with precise temporal and spatial control. Understanding these proteomic networks are essential to addressing ongoing global crises, including those of food security, rising global temperatures, and the need for renewable materials and fuels. Technological advances in mass spectrometry-based proteomics are enabling investigations of unprecedented depth, and are increasingly being optimized for and applied to plant systems. This review highlights recent advances in plant proteomics, with an emphasis on spatially and temporally resolved analysis of post-translational modifications and protein interactions. It also details the necessity for generation of a comprehensive plant cell atlas while highlighting recent accomplishments within the field.

Mapping the SARS-CoV-2-Host Protein-Protein Interactome by Affinity Purification Mass Spectrometry and Proximity-Dependent Biotin Labeling: A Rational and Straightforward Route to Discover Host-Directed Anti-SARS-CoV-2 Therapeutics

Protein-protein interactions (PPIs) are the vital engine of cellular machinery. After virus entry in host cells the global organization of the viral life cycle is strongly regulated by the formation of virus-host protein interactions. With the advent of high-throughput -omics platforms, the mirage to obtain a "high resolution" view of virus-host interactions has come true. In fact, the rapidly expanding approaches of mass spectrometry (MS)-based proteomics in the study of PPIs provide efficient tools to identify a significant number of potential drug targets. Generation of PPIs maps by affinity purification-MS and by the more recent proximity labeling-MS may help to uncover cellular processes hijacked and/or altered by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), providing promising therapeutic targets. The possibility to further validate putative key targets from high-confidence interactions between viral bait and host protein through follow-up MS-based multi-omics experiments offers an unprecedented opportunity in the drug discovery pipeline. In particular, drug repurposing, making use of already existing approved drugs directly targeting these identified and validated host interactors, might shorten the time and reduce the costs in comparison to the traditional drug discovery process. This route might be promising for finding effective antiviral therapeutic options providing a turning point in the fight against the coronavirus disease-2019 (COVID-19) outbreak.

Affinity proteomic dissection of the human nuclear cap-binding complex interactome

A 5′,7-methylguanosine cap is a quintessential feature of RNA polymerase II-transcribed RNAs, and a textbook aspect of co-transcriptional RNA processing. The cap is bound by the cap-binding complex (CBC), canonically consisting of nuclear cap-binding proteins 1 and 2 (NCBP1/2). Interest in the CBC has recently renewed due to its participation in RNA-fate decisions via interactions with RNA productive factors as well as with adapters of the degradative RNA exosome. A novel cap-binding protein, NCBP3, was recently proposed to form an alternative CBC together with NCBP1, and to interact with the canonical CBC along with the protein SRRT. The theme of post-transcriptional RNA fate, and how it relates to co-transcriptional ribonucleoprotein assembly, is abundant with complicated, ambiguous, and likely incomplete models. In an effort to clarify the compositions of NCBP1-, 2- and 3-related macromolecular assemblies, we have applied an affinity capture-based interactome screen where the experimental design and data processing have been modified to quantitatively identify interactome differences between targets under a range of experimental conditions. This study generated a comprehensive view of NCBP-protein interactions in the ribonucleoprotein context and demonstrates the potential of our approach to benefit the interpretation of complex biological pathways.

Proteomic Applications in Antimicrobial Resistance and Clinical Microbiology Studies

Sequences of the genomes of all-important bacterial pathogens of man, plants, and animals have been completed. Still, it is not enough to achieve complete information of all the mechanisms controlling the biological processes of an organism. Along with all advances in different proteomics technologies, proteomics has completed our knowledge of biological processes all around the world. Proteomics is a valuable technique to explain the complement of proteins in any organism. One of the fields that has been notably benefited from other systems approaches is bacterial pathogenesis. An emerging field is to use proteomics to examine the infectious agents in terms of, among many, the response the host and pathogen to the infection process, which leads to a deeper knowledge of the mechanisms of bacterial virulence. This trend also enables us to identify quantitative measurements for proteins extracted from microorganisms. The present review study is an attempt to summarize a variety of different proteomic techniques and advances. The significant applications in bacterial pathogenesis studies are also covered. Moreover, the areas where proteomics may lead the future studies are introduced.

Methods of Computational Interactomics for Investigating Interactions of Human Proteoforms

Human genome contains ca. 20,000 protein-coding genes that could be translated into millions of unique protein species (proteoforms). Proteoforms coded by a single gene often have different functions, which implies different protein partners. By interacting with each other, proteoforms create a network reflecting the dynamics of cellular processes in an organism. Perturbations of protein-protein interactions change the network topology, which often triggers pathological processes. Studying proteoforms is a relatively new research area in proteomics, and this is why there are comparatively few experimental studies on the interaction of proteoforms. Bioinformatics tools can facilitate such studies by providing valuable complementary information to the experimental data and, in particular, expanding the possibilities of the studies of proteoform interactions.

Affinity Proteomic Analysis of the Human Exosome and Its Cofactor Complexes

In humans, the RNA exosome consists of an enzymatically inactive nine-subunit core, with ribonucleolytic activity contributed by additional components. Several cofactor complexes also interact with the exosome-these enable the recruitment of, and specify the activity upon, diverse substrates. Affinity capture coupled with mass spectrometry has proven to be an effective means to identify the compositions of RNA exosomes and their cofactor complexes: here, we describe a general experimental strategy for proteomic characterization of macromolecular complexes, applied to the exosome and an affiliated adapter protein, ZC3H18.

Co-complex protein membership evaluation using Maximum Entropy on GO ontology and InterPro annotation

Motivation

Protein–protein interactions (PPI) play a crucial role in our understanding of protein function and biological processes. The standardization and recording of experimental findings is increasingly stored in ontologies, with the Gene Ontology (GO) being one of the most successful projects. Several PPI evaluation algorithms have been based on the application of probabilistic frameworks or machine learning algorithms to GO properties. Here, we introduce a new training set design and machine learning based approach that combines dependent heterogeneous protein annotations from the entire ontology to evaluate putative co-complex protein interactions determined by empirical studies.

Results

PPI annotations are built combinatorically using corresponding GO terms and InterPro annotation. We use a S.cerevisiae high-confidence complex dataset as a positive training set. A series of classifiers based on Maximum Entropy and support vector machines (SVMs), each with a composite counterpart algorithm, are trained on a series of training sets. These achieve a high performance area under the ROC curve of ≤0.97, outperforming go2ppi—a previously established prediction tool for protein-protein interactions (PPI) based on Gene Ontology (GO) annotations.

Availability and implementation

https://github.com/ima23/maxent-ppi

Supplementary information

Supplementary data are available at Bioinformatics online.

Experimental Analysis of Viral-Host Interactions

Viral and pathogen protein complexity is often limited by their relatively small genomes, thus critical functions are often accomplished by complexes of host and pathogen proteins. This requirement makes the study of host-pathogen interactions critical for the understanding of pathogenicity and virology. This review article discusses proteomic methods that offer an opportunity to experimentally identify and analyze the binding partners of a target protein and presents the representative studies performed with these methods. These methods divide into two classes: ex situ and in situ. Ex situ assays depend on bindings that occur outside of the normal cellular environment and include yeast two hybrids, pull-downs, and nucleic acid-programmable protein arrays (NAPPA). In situ assays depend on bindings that occur inside of host cells and include affinity purification (AP) and proximity dependent labeling (PDL). Either ex or in situ methods can be reliably used for generating protein-protein interactions networks but it is important to understand and recognize the limitations of the chosen methods when developing an interactomic network. In summary, proteomic methods can be extremely useful for interactomics but it is important to recognize the nature of the method when designing and analyzing an experiment.

Identification of RNase-sensitive LINE-1 Ribonucleoprotein Interactions by Differential Affinity Immobilization

Long Interspersed Nuclear Element-1 (LINE-1, L1) constitutes a family of autonomous, self-replicating genetic elements known as retrotransposons. Although most are inactive, copious L1 sequences populate the human genome. L1s proliferate in a 'copy-and-paste' fashion through an RNA intermediate; a full-length L1 transcript is ~6,000 nucleotides long and functions as a bicistronic mRNA that encodes and assembles in cis with two main polypeptides, ORF1p and ORF2p, forming a ribonucleoprotein (RNP); L1 RNPs also interact with a wide range of host factors in positive and negative regulatory capacities. The following protocol describes an approach to affinity enrich ectopically expressed L1 RNPs and, using RNases, release the fraction of protein that depends upon the presence of intact RNA for retention in the immobilized macromolecules.

Exploring the protein-protein interaction landscape in plants

Protein-protein interactions (PPIs) represent an essential aspect of plant systems biology. Identification of key protein players and their interaction networks provide crucial insights into the regulation of plant developmental processes and into interactions of plants with their environment. Despite the great advance in the methods for the discovery and validation of PPIs, still several challenges remain. First, the PPI networks are usually highly dynamic, and the in vivo interactions are often transient and difficult to detect. Therefore, the properties of the PPIs under study need to be considered to select the most suitable technique, because each has its own advantages and limitations. Second, besides knowledge on the interacting partners of a protein of interest, characteristics of the interaction, such as the spatial or temporal dynamics, are highly important. Hence, multiple approaches have to be combined to obtain a comprehensive view on the PPI network present in a cell. Here, we present the progress in commonly used methods to detect and validate PPIs in plants with a special emphasis on the PPI features assessed in each approach and how they were or can be used for the study of plant interactions with their environment.

Considerations for Identifying Endogenous Protein Complexes from Tissue via Immunoaffinity Purification and Quantitative Mass Spectrometry

Protein complexes perform key roles in nearly all aspects of biology. Identification of the composition of these complexes offers insights into how different cellular processes are carried out. The use of affinity purification coupled to mass spectrometry has become a method of choice for identifying protein-protein interactions, but has been most frequently applied to cell model systems using tagged and overexpressed bait proteins. Although valuable, this approach can create several potential artifacts due to the presence of a tag on a protein and the higher abundance of the protein of interest (bait). The isolation of endogenous proteins using antibodies raised against the proteins of interest instead of an epitope tag offers a means to examine protein interactions in any cellular or animal model system and without the caveats of overexpressed, tagged proteins. Although conceptually simple, the limited use of this approach has been primarily driven by challenges associated with finding adequate antibodies and experimental conditions for effective isolations. In this chapter, we present a protocol for the optimization of lysis conditions, antibody evaluation, affinity purification, and ultimately identification of protein complexes from endogenous immunoaffinity purifications using quantitative mass spectrometry. We also highlight the increased use of targeted mass spectrometry analyses, such as parallel reaction monitoring (PRM) for orthogonal validation of protein isolation and interactions initially identified via data-dependent mass spectrometry analyses.

Getting to know the neighborhood: using proximity-dependent biotinylation to characterize protein complexes and map organelles

The use of proximity-dependent biotinylation approaches combined with mass spectrometry (e.g. BioID and APEX) has revolutionized the study of protein-protein interactions and organellar proteomics. These powerful techniques are based on the fusion of an enzyme (e.g. a biotin ligase or peroxidase) to a 'bait' protein of interest, which is then expressed in a relevant biological setting. Addition of enzyme substrate enables covalent biotin labeling of proteins in the vicinity of the bait in vivo. These approaches thus allow for the capture and identification of 'neighborhood' proteins in the context of a living cell, and provide data that are complementary to more established techniques such as fractionation or affinity purification. As compared to standard affinity-based purification approaches, proximity-dependent biotinylation (PDB) can help to: first, identify interactions with and amongst membrane proteins, and other polypeptide classes that are less amenable to study by standard pulldown techniques; second, enrich for transient and/or low affinity interactions that are not readily captured using affinity purification approaches; third, avoid post-lysis artefacts associated with standard biochemical purification experiments and; fourth, provide deep insight into the organization of membrane-less organelles and other subcellular structures that cannot be easily isolated or purified. Given the increasing use of these techniques to answer a variety of different types of biological questions, it is important to understand how best to design PDB-MS experiments, what type of data they generate, and how to analyze and interpret the results.

Global landscape of cell envelope protein complexes in Escherichia coli

Bacterial cell envelope protein (CEP) complexes mediate a range of processes, including membrane assembly, antibiotic resistance and metabolic coordination. However, only limited characterization of relevant macromolecules has been reported to date. Here we present a proteomic survey of 1,347 CEPs encompassing 90% inner- and outer-membrane and periplasmic proteins of Escherichia coli. After extraction with non-denaturing detergents, we affinity-purified 785 endogenously tagged CEPs and identified stably associated polypeptides by precision mass spectrometry. The resulting high-quality physical interaction network, comprising 77% of targeted CEPs, revealed many previously uncharacterized heteromeric complexes. We found that the secretion of autotransporters requires translocation and the assembly module TamB to nucleate proper folding from periplasm to cell surface through a cooperative mechanism involving the β-barrel assembly machinery. We also establish that an ABC transporter of unknown function, YadH, together with the Mla system preserves outer membrane lipid asymmetry. This E. coli CEP 'interactome' provides insights into the functional landscape governing CE systems essential to bacterial growth, metabolism and drug resistance.

A two-step framework for inferring direct protein-protein interaction network from AP-MS data

Background

Affinity purification-mass spectrometry (AP-MS) has been widely used for generating bait-prey data sets so as to identify underlying protein-protein interactions and protein complexes. However, the AP-MS data sets in terms of bait-prey pairs are highly noisy, where candidate pairs contain many false positives. Recently, numerous computational methods have been developed to identify genuine interactions from AP-MS data sets. However, most of these methods aim at removing false positives that contain contaminants, ignoring the distinction between direct interactions and indirect interactions.

Results

In this paper, we present an initialization-and-refinement framework for inferring direct PPI networks from AP-MS data, in which an initial network is first generated with existing scoring methods and then a refined network is constructed by the application of indirect association removal methods. Experimental results on several real AP-MS data sets show that our method is capable of identifying more direct interactions than traditional scoring methods.

Conclusions

The proposed framework is sufficiently general to incorporate any feasible methods in each step so as to have potential for handling different types of AP-MS data in the future applications.

Electronic supplementary material

The online version of this article (doi:10.1186/s12918-017-0452-y) contains supplementary material, which is available to authorized users.

Protein complex analysis: From raw protein lists to protein interaction networks

The elucidation of molecular interaction networks is one of the pivotal challenges in the study of biology. Affinity purification-mass spectrometry and other co-complex methods have become widely employed experimental techniques to identify protein complexes. These techniques typically suffer from a high number of false negatives and false positive contaminants due to technical shortcomings and purification biases. To support a diverse range of experimental designs and approaches, a large number of computational methods have been proposed to filter, infer and validate protein interaction networks from experimental pull-down MS data. Nevertheless, this expansion of available methods complicates the selection of the most optimal ones to support systems biology-driven knowledge extraction. In this review, we give an overview of the most commonly used computational methods to process and interpret co-complex results, and we discuss the issues and unsolved problems that still exist within the field. © 2015 Wiley Periodicals, Inc. Mass Spec Rev 36:600-614, 2017.

Proteomics and integrative omic approaches for understanding host-pathogen interactions and infectious diseases

Organisms are constantly exposed to microbial pathogens in their environments. When a pathogen meets its host, a series of intricate intracellular interactions shape the outcome of the infection. The understanding of these host–pathogen interactions is crucial for the development of treatments and preventive measures against infectious diseases. Over the past decade, proteomic approaches have become prime contributors to the discovery and understanding of host–pathogen interactions that represent anti‐ and pro‐pathogenic cellular responses. Here, we review these proteomic methods and their application to studying viral and bacterial intracellular pathogens. We examine approaches for defining spatial and temporal host–pathogen protein interactions upon infection of a host cell. Further expanding the understanding of proteome organization during an infection, we discuss methods that characterize the regulation of host and pathogen proteomes through alterations in protein abundance, localization, and post‐translational modifications. Finally, we highlight bioinformatic tools available for analyzing such proteomic datasets, as well as novel strategies for integrating proteomics with other omic tools, such as genomics, transcriptomics, and metabolomics, to obtain a systems‐level understanding of infectious diseases.

Mapping Protein-Protein Interactions Using Affinity Purification and Mass Spectrometry

The mapping of protein-protein interaction (PPI) networks and their dynamics are crucial steps to deciphering the function of a protein and its role in cellular pathways, making it critical to have comprehensive knowledge of a protein's interactome. Advances in affinity purification and mass spectrometry technology (AP-MS) have provided a powerful and unbiased method to capture higher-order protein complexes and decipher dynamic PPIs. However, the unbiased calling of nonspecific interactions and the ability to detect transient interactions remains challenging when using AP-MS, thereby hampering the detection of biologically meaningful complexes. Additionally, there are plant-specific challenges with AP-MS, such as a lack of protein-specific antibodies, which must be overcome to successfully identify PPIs. Here we discuss and describe a protocol designed to bypass the traditional challenges of AP-MS and provide a roadmap to identify bona fide PPIs in plants.

Protein Complex Affinity Capture from Cryomilled Mammalian Cells

Affinity capture is an effective technique for isolating endogenous protein complexes for further study. When used in conjunction with an antibody, this technique is also frequently referred to as immunoprecipitation. Affinity capture can be applied in a bench-scale and in a high-throughput context. When coupled with protein mass spectrometry, affinity capture has proven to be a workhorse of interactome analysis. Although there are potentially many ways to execute the numerous steps involved, the following protocols implement our favored methods. Two features are distinctive: the use of cryomilled cell powder to produce cell extracts, and antibody-coupled paramagnetic beads as the affinity medium. In many cases, we have obtained superior results to those obtained with more conventional affinity capture practices. Cryomilling avoids numerous problems associated with other forms of cell breakage. It provides efficient breakage of the material, while avoiding denaturation issues associated with heating or foaming. It retains the native protein concentration up to the point of extraction, mitigating macromolecular dissociation. It reduces the time extracted proteins spend in solution, limiting deleterious enzymatic activities, and it may reduce the non-specific adsorption of proteins by the affinity medium. Micron-scale magnetic affinity media have become more commonplace over the last several years, increasingly replacing the traditional agarose- and Sepharose-based media. Primary benefits of magnetic media include typically lower non-specific protein adsorption; no size exclusion limit because protein complex binding occurs on the bead surface rather than within pores; and ease of manipulation and handling using magnets.

TBMS1 exerts its cytotoxicity in NCI-H460 lung cancer cells through nucleolar stress-induced p53/MDM2-dependent mechanism, a quantitative proteomics study

Tubeimoside-1 (TBMS1) exerts its anticancer effects by inducing G2/M arrest and apoptosis of cancer cells. However, the precise molecular mechanism of its anti-tumor effects has not been fully elucidated, especially the signaling pathways involved in the early stage of TBMS1 stimulation. In this study, we employed stable isotope labeling by amino acids in cell culture (SILAC)-based quantitative proteomics approach and identified 439 proteins that exhibit significant differential expressions in NCI-H460 lung cancer cells upon exposure to TBMS1. Gene ontology and network analysis using DAVID and STRING on-line tools revealed that several nucleolar stress (ribosomal biogenesis) response proteins were differentially regulated by TBMS1. Functional validation demonstrated that TBMS1-induced NCI-H460 cell cytotoxicity involved nucleolar stress-induced p53/murine double minute clone 2 (MDM2), mTOR, and NF-κB signaling pathways.

Differential connectivity of splicing activators and repressors to the human spliceosome

Background

During spliceosome assembly, protein-protein interactions (PPI) are sequentially formed and disrupted to accommodate the spatial requirements of pre-mRNA substrate recognition and catalysis. Splicing activators and repressors, such as SR proteins and hnRNPs, modulate spliceosome assembly and regulate alternative splicing. However, it remains unclear how they differentially interact with the core spliceosome to perform their functions.

Results

Here, we investigate the protein connectivity of SR and hnRNP proteins to the core spliceosome using probabilistic network reconstruction based on the integration of interactome and gene expression data. We validate our model by immunoprecipitation and mass spectrometry of the prototypical splicing factors SRSF1 and hnRNPA1. Network analysis reveals that a factor’s properties as an activator or repressor can be predicted from its overall connectivity to the rest of the spliceosome. In addition, we discover and experimentally validate PPIs between the oncoprotein SRSF1 and members of the anti-tumor drug target SF3 complex. Our findings suggest that activators promote the formation of PPIs between spliceosomal sub-complexes, whereas repressors mostly operate through protein-RNA interactions.

Conclusions

This study demonstrates that combining in-silico modeling with biochemistry can significantly advance the understanding of structure and function relationships in the human spliceosome.

Electronic supplementary material

The online version of this article (doi:10.1186/s13059-015-0682-5) contains supplementary material, which is available to authorized users.

Rapid, optimized interactomic screening

We must reliably map the interactomes of cellular macromolecular complexes in order to fully explore and understand biological systems. However, there are no methods to accurately predict how to capture a given macromolecular complex with its physiological binding partners. Here, we present a screen that comprehensively explores the parameters affecting the stability of interactions in affinity-captured complexes, enabling the discovery of physiological binding partners and the elucidation of their functional interactions in unparalleled detail. We have implemented this screen on several macromolecular complexes from a variety of organisms, revealing novel profiles even for well-studied proteins. Our approach is robust, economical and automatable, providing an inroad to the rigorous, systematic dissection of cellular interactomes.

Extracting high confidence protein interactions from affinity purification data: at the crossroads

Deriving protein-protein interactions from data generated by affinity-purification and mass spectrometry (AP-MS) techniques requires application of scoring methods to measure the reliability of detected putative interactions. Choosing the appropriate scoring method has become a major challenge. Here we apply six popular scoring methods to the same AP-MS dataset and compare their performance. The comparison was carried out for six distinct datasets from human, fly and yeast, which focus on different biological processes and differ in their coverage of the proteome. Results show that the performance of a given scoring method may vary substantially depending on the dataset. Disturbingly, we find that the high confidence (HC) PPI networks built by applying the six scoring methods to the same raw AP-MS dataset display very poor overlap, with only 1.7-4.1% of the HC interactions present in all the networks built, respectively, from the proteome-wide human, fly or yeast datasets. Various properties of the shared versus unique interactions in each network, including biases in protein abundance, suggest that current scoring methods are able to eliminate only the most obvious contaminants, but still fail to reliably single out specific interactions from the large body of spurious associations detected in the AP-MS experiments.

BIOLOGICAL SIGNIFICANCE

The fast progress in AP-MS techniques has prompted the development of a multitude of scoring methods, which are relied upon to remove contaminants and non-specific binders. Choosing the appropriate scoring scheme for a given AP-MS dataset has become a major challenge. The comparative analysis of 6 of the most popular scoring methods, presented here, reveals that overall these methods do not perform as expected. Evidence is provided that this is due to 3 closely related issues: the high 'noise' levels of the raw AP-MS data, the limited capacity of current scoring methods to deal with such high noise levels, and the biases introduced using Gold Standard datasets to benchmark the scoring functions and threshold the networks. For the field to move forward, all three issues will have to be addressed. This article is part of a Special Issue entitled: Protein dynamics in health and disease. Guest Editors: Pierre Thibault and Anne-Claude Gingras.

Protein networks and activation of lymphocytes

The signal transduction pathways initiated by lymphocyte activation play a critical role in regulating host immunity. High-resolution mass spectrometry has accelerated the investigation of these complex and dynamic pathways by enabling the qualitative and quantitative investigation of thousands of proteins and phosphoproteins simultaneously. In addition, the unbiased and wide-scale identification of protein-protein interaction networks and protein kinase substrates in lymphocyte signaling pathways can be achieved by mass spectrometry-based approaches. Critically, the integration of these discovery-driven strategies with single-cell analysis using mass cytometry can facilitate the understanding of complex signaling phenotypes in distinct immunophenotypes.

Merging and scoring molecular interactions utilising existing community standards: tools, use-cases and a case study

The evidence that two molecules interact in a living cell is often inferred from multiple different experiments. Experimental data is captured in multiple repositories, but there is no simple way to assess the evidence of an interaction occurring in a cellular environment. Merging and scoring of data are commonly required operations after querying for the details of specific molecular interactions, to remove redundancy and assess the strength of accompanying experimental evidence. We have developed both a merging algorithm and a scoring system for molecular interactions based on the proteomics standard initiative–molecular interaction standards. In this manuscript, we introduce these two algorithms and provide community access to the tool suite, describe examples of how these tools are useful to selectively present molecular interaction data and demonstrate a case where the algorithms were successfully used to identify a systematic error in an existing dataset.

CHD6 regulates the topological arrangement of the CFTR locus

The control of transcription is regulated through the well-coordinated spatial and temporal interactions between distal genomic regulatory elements required for specialized cell-type and developmental gene expression programs. With recent findings CFTR has served as a model to understand the principles that govern genome-wide and topological organization of distal intra-chromosomal contacts as it relates to transcriptional control. This is due to the extensive characterization of the DNase hypersensitivity sites, modification of chromatin, transcription factor binding sites and the arrangement of these sites in CFTR consistent with the restrictive expression in epithelial cell types. Here, we identified CHD6 from a screen among several chromatin-remodeling proteins as a putative epigenetic modulator of CFTR expression. Moreover, our findings of CTCF interactions with CHD6 are consistent with the role described previously for CTCF in CFTR regulation. Our results now reveal that the CHD6 protein lies within the infrastructure of multiple transcriptional complexes, such as the FACT, PBAF, PAF1C, Mediator, SMC/Cohesion and MLL complexes. This model underlies the fundamental role CHD6 facilitates by tethering cis-acting regulatory elements of CFTR in proximity to these multi-subunit transcriptional protein complexes. Finally, we indicate that CHD6 structurally coordinates a three-dimensional stricture between intragenic elements of CFTR bound by several cell-type specific transcription factors, such as CDX2, SOX18, HNF4α and HNF1α. Therefore, our results reveal new insights into the epigenetic regulation of CFTR expression, whereas the manipulation of CFTR gene topology could be considered for treating specific indications of cystic fibrosis and/or pancreatitis.

Phosphoproteomics in cereals

Cereals are the most important crop plant supplying staple food throughout the world. The economic importance and continued breeding of crop plants such as rice, maize, wheat, or barley require a detailed scientific understanding of adaptive and developmental processes. Protein phosphorylation is one of the most important regulatory posttranslational modifications and its analysis allows deriving functional and regulatory principles in plants. This minireview summarizes the current knowledge of phosphoproteomic studies in cereals.

Comparative Genomics and Evolutionary Modularity of Prokaryotes

The soaring number of high-quality genomic sequences has ushered in the era of post-genomic research where our understanding of organisms has dramatically shifted towards defining the function of genes within their larger biological contexts. As a result, novel high-throughput experimental technologies are being increasingly employed to uncover physical and functional associations of genes and proteins in complex biological processes. Through the construction and analysis of physical, genetic and metabolic networks generated for the model organisms, such as Escherichia coli, organizational principles of the genome have been deduced, such as modularity, which has important implications toward understanding prokaryotic evolution and adaptation to novel lifestyles.

SILAC-iPAC: a quantitative method for distinguishing genuine from non-specific components of protein complexes by parallel affinity capture

Pull-down assays can identify members of protein complexes but suffer from co-isolation of contaminants. The problem is particularly acute when the specifically interacting partners are of low-abundance and/or bind transiently with low affinity. To differentiate true interacting partners from contaminants, we have combined SILAC labelling with a proteomic method called “Interactomes by Parallel Affinity Capture” (iPAC). In our method, a cell-line stably expressing a doubly tagged target endogenous protein and its tag-less control cell-line are differentially SILAC labelled. Lysates from the two cell-lines are mixed and the tagged protein is independently purified for MS analysis using multiple affinity resins in parallel. This allows the quantitative identification of tagged proteins and their binding partners. SILAC–iPAC provides a rigorous and sensitive approach that can discriminate between genuine binding partners and contaminants, even when the contaminants in the pull-down are in large excess. We employed our method to examine the interacting partners of phosphatidyl inositol 5-phosphate 4-kinase 2β subunit (PI5P4K2β) and the Fanconi anaemia core complex in the chicken pre-B cell-line DT40. We confirmed known components of these two complexes, and we have identified new potential binding partners. Combining the iPAC approach with SILAC labelling provides a sensitive and fully quantitative method for the discrimination of specific interactions under conditions where low signal to noise ratios are unavoidable. In addition, our work provides the first characterisation of the most abundant proteins within the DT40 proteome and the non-specific DT40 ‘beadomes’ (non-specific proteins binding to beads) for common epitope tags. Given the importance and widespread use of the DT40 cell-line, these will be important resources for the cell biology and immunology communities.

Biological significance

SILAC–iPAC provides an improved method for the analysis of low-affinity and/or low abundance protein-protein interactions. We use it to clarify two examples where the nature of the protein complexes are known, or are currently unclear. The method is simple and quantitative and will be applicable to many problems in cell and molecular biology. We also report the first chicken beadomes.

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SILAC–iPAC; an improved AP-MS method to quantitatively detect low abundance proteins

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RUVBL1 and its partner RUVBL2 are novel interactors of the Fanconi anaemia complex.

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First characterisation of chicken DT40 beadomes using four common epitope tags

UHPLC-LTQ-Orbitrap MS combined with spike-in method for plasma metabonomics analysis of acute myocardial ischemia rats and pretreatment effect of Danqi Tongmai tablet

Undoubtedly, metabonomics can reveal the comprehensive efficacies of traditional Chinese medicine (TCM) formulae and its complex mechanism at the molecular biological level. In this study, an attempt was made to address the pretreatment effect of a TCM formula. In this case, as a critical point, we should first know how to really reflect the various endogenous metabolites in a disease status before a TCM formula is employed in a therapeutic procedure. Here, we explored an approach that combined high resolution LTQ-Orbitrap mass spectrometry with a spike-in method to characterize endogenous metabolites in acute myocardial ischemia (AMI) rats. As a result, 19 potential biomarkers in rat plasma were identified and 10 related disturbed pathways were perturbed in the early stages of AMI development. Subsequently, the metabonomics method was applied to investigate the pretreatment effect of the TCM formula named the Danqi Tongmai tablet (DQTM). The results revealed that the DQTM pretreatment could reduce the AMI injury and partially regulate the perturbed TCA cycle and amino and nucleotide metabolism, which were presumable related to energy metabolism and myocardial cells apoptosis/necrosis. In conclusion, UHPLC-LTQ-Orbitrap MS combined with a spike-in method were successfully applied to the metabonomics analysis of DQTM, which demonstrated that not only a comprehensive metabolic profile in the early stages of AMI development was achieved, but also that the underlying holistic efficacies were assessed and it was helpful to understand the possible mechanism of pretreatment with DQTM.

From proteomes to complexomes in the era of systems biology

Protein complexes carry out almost the entire signaling and functional processes in the cell. The protein complex complement of a cell, and its network of complex-complex interactions, is referred to here as the complexome. Computational methods to predict protein complexes from proteomics data, resulting in network representations of complexomes, have recently being developed. In addition, key advances have been made toward understanding the network and structural organization of complexomes. We review these bioinformatics advances, and their discovery-potential, as well as the merits of integrating proteomics data with emerging methods in systems biology to study protein complex signaling. It is envisioned that improved integration of proteomics and systems biology, incorporating the dynamics of protein complexes in space and time, may lead to more predictive models of cell signaling networks for effective modulation.

Pre- and post-processing workflow for affinity purification mass spectrometry data

The reliable detection of protein-protein interactions by affinity purification mass spectrometry (AP-MS) is crucial for the understanding of biological processes. Quantitative information can be used to separate truly interacting proteins from false-positives by contrasting counts of proteins binding to specific baits with counts of negative controls. Several approaches have been proposed for computing scores for potential interaction proteins, for example, the commonly used SAINT software. However, it remains a subjective decision where to set the cutoff score for candidate selection; furthermore, no precise control for the expected number of false-positives is provided. In related fields, successful data analysis strongly relies on statistical pre- and post-processing steps, which, so far, have played only a minor role in AP-MS data analysis. We introduce a complete workflow, embedding either the scoring method SAINT or alternatively a two-stage Poisson model into a pre- and post-processing framework. To this end, we investigate different normalization methods and apply a statistical filter adjusted to AP-MS data. Furthermore, we propose permutation and adjustment procedures, which allow the replacement of scores by statistical p values. The performance of the workflow is assessed on simulations as well as on a study focusing on interactions with the T3SS in Salmonella Typhimurium. Preprocessing methods significantly increase the number of detected truly interacting proteins, while a constant false-discovery rate is maintained. The software solution is freely available.

Phosphoinositide-specific phospholipase C β 1b (PI-PLCβ1b) interactome: affinity purification-mass spectrometry analysis of PI-PLCβ1b with nuclear protein

Two isoforms of inositide-dependent phospholipase C β1 (PI-PLCβ1) are generated by alternative splicing (PLCβ1a and PLCβ1b). Both isoforms are present within the nucleus, but in contrast to PLCβ1a, the vast majority of PLCβ1b is nuclear. In mouse erythroid leukemia cells, PI-PLCβ1 is involved in the regulation of cell division and the balance between cell proliferation and differentiation. It has been demonstrated that nuclear localization is crucial for the enzymatic function of PI-PLCβ1, although the mechanism by which this nuclear import occurs has never been fully characterized. The aim of this study was to characterize both the mechanism of nuclear localization and the molecular function of nuclear PI-PLCβ1 by identifying its interactome in Friend's erythroleukemia isolated nuclei, utilizing a procedure that coupled immuno-affinity purification with tandem mass spectrometry analysis. Using this procedure, 160 proteins were demonstrated to be in association with PI-PLCβ1b, some of which have been previously characterized, such as the splicing factor SRp20 (Srsf3) and Lamin B (Lmnb1). Co-immunoprecipitation analysis of selected proteins confirmed the data obtained via mass spectrometry. Of particular interest was the identification of the nuclear import proteins Kpna2, Kpna4, Kpnb1, Ran, and Rangap1, as well as factors involved in hematological malignancies and several anti-apoptotic proteins. These data give new insight into possible mechanisms of nuclear trafficking and functioning of this critical signaling molecule.